docs: def KGE(s, o):
      """
          Kling-Gupta Efficiency
          input:
          s: simulated
          o: observed
          output:
          kge: Kling-Gupta Efficiency
          cc: correlation
          alpha: ratio of the standard deviation
          beta: ratio of the mean
          """
      # s,o = filter_nan(s, o)
      cc = correlation(s, o)
      alpha = np.std(s)/np.std(o)
      beta = np.sum(s)/np.sum(o)
      kge = 1 - np.sqrt((cc-1)**2 + (alpha-1)**2 + (beta-1)**2)
      return kge, cc, alpha, beta